A comparative study of a theoretical neural net model with MEG data from epileptic patients and normal individuals

MEG as a Medical Diagnostic Tool in the Greek Population

Magnetoencephalography (MEG) is the recording of the magnetic field produced by the flowing of ions in the brain. This article reports our experience in the application of MEG in patients and healthy volunteers in the Greek population. We provide a brief description of our research work. The MEG data were recorded in a magnetically shielded room with a whole-head 122 channel or an one-channel biomagnetometer. Our results lead us to believe that the MEG is an important research field which is evolving quickly with a number of interesting findings with respect to normal and abnormal functions of the human brain. It could provide clinical practice with an easy to perform non invasive method, which could be adjunct to conventional methods for the evaluation of brain disorders.

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.